# 如何使用OpenAI、LangChain和LlamaIndex（GPT Index）构建自定义的DevSecOps知识库，输入文件是DevSecOps相关的pdf文件输入。
from llama_index import StorageContext, ServiceContext, GPTVectorStoreIndex, LLMPredictor, PromptHelper, SimpleDirectoryReader, load_index_from_storage
from langchain.chat_models import ChatOpenAI
import gradio as gr
import sys
import os

# 设置自己的openAI 的key，放到 env 中
os.environ["OPENAI_API_KEY"] = 'YOUR-OPENAI-API-KEY'


def create_service_context():
    #constraint parameters
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 20
    chunk_size_limit = 600
    #allows the user to explicitly set certain constraint parameters
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
    #LLMPredictor is a wrapper class around LangChain's LLMChain that allows easy integration into LlamaIndex
    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
    #constructs service_context
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    return service_context

# 该函数负责提取数据并创建并保存用于数据查询的索引，以供知识库使用。
def data_ingestion_indexing(directory_path):
    #loads data from the specified directory path
    documents = SimpleDirectoryReader(directory_path).load_data()
    
    #when first building the index
    index = GPTVectorStoreIndex.from_documents(
        documents, service_context=create_service_context()
    )
    #persist index to disk, default "storage" folder
    index.storage_context.persist()
    return index


def data_querying(input_text):
    #rebuild storage context
    storage_context = StorageContext.from_defaults(persist_dir="./storage")
    #loads index from storage
    index = load_index_from_storage(storage_context, service_context=create_service_context())
    
    #queries the index with the input text
    response = index.as_query_engine().query(input_text)
    
    return response.response


iface = gr.Interface(fn=data_querying,
    inputs=gr.components.Textbox(lines=7, label="Enter your text"),
    outputs="text",
    title="Wenqi's Custom-trained DevSecOps Knowledge Base")